1. Field of the Invention
This invention relates to a method for automatically adjusting characteristics of a reproducing signal processing apparatus in a digital recording and reproducing system or in a digital transmission system using a magnetic recording medium and, in particular, for automatically adjusting characteristics of an automatic equalizer for converting a reproduced signal into a digital signal.
2. Description of the Prior Art
In a digital recording and reproducing system or a digital transmission system, an increase in the recording density causes a decrease in the S/N ratio of a signal reproduced from a magnetic recording medium (magnetic tape, magnetic disk and so on), and increases such errors so that the reproduced digital signal does not correspond to the recorded digital signal. There is the need for a signal processing technique that properly detects a digital signal from an analog waveform from a reproducing head.
A known existing signal processing technique available for this purpose is an integral detecting method. This method executes equalization based on an equalizing standard, called the Nyquist standard, to minimize intersymbol interference at an identified point and then binarizes the Nyquist waveform in view of the value relative to a predetermined threshold value.
With respect to characteristics of an equalizer for equalization referred to above, parameters are fixed after being adjusted by using a test signal so as to minimize code errors. Nevertheless, code errors increase with variations or changes with time in an electromagnetic converter system including tape and heads. When a scheme that sets parameters to optimal values by using a test signal is compared with a scheme that sets parameters automatically, there are slightly more errors in the automatic setting scheme. However, automatic adjustment of parameters copes with varieties of tape and heads or their changes with time. Taking this into consideration, automatic setting of parameters can reduce the error rate in the long-range view than the longtime use of parameters manually set to minimize errors. Therefore, the equalizer is desired to be an automatic equalizer whose characteristics are adjusted automatically in accordance with a reproduced signal. There is the zero forcing algorithm as a procedure for realizing such an automatic equalizer. This procedure calculates equalization errors successively and reestablishes a tap coefficient of, for example, a transversal filter, in a direction for minimizing the errors.
Explained below is a case where the zero forcing algorithm is employed in a three-tap transversal filter. Output of the three-tap transversal filter is as follows: EQU X(k)=C(-1).times.Y(k-1)+C(0).times.Y(k)+C(1).times.Y(k+1)
where
Y(k): amplitude of a reproduced signal of a kth bit before equalization PA1 X(k): amplitude of the reproduced signal of the kth bit after equalization PA1 C(O): coefficient for a current bit PA1 C(-1): tap coefficient for a preceding bit PA1 C(1): tap coefficient for a subsequent bit PA1 E(k): equalization error of the kth bit PA1 B(k): expected amplitude value obtained from a result of detection of the kth bit PA1 T: threshold value for integral detection PA1 .SIGMA.: sum of (k=0) to (k=N) PA1 A(k): result of identification of the kth bit (1 or -1) PA1 .alpha..gtoreq.0 : sgn(.alpha.)=1 PA1 .alpha.&lt;0 : sgn(.alpha.)=-1 PA1 A. clearing an equalization error estimation value of an identification value created by the automatic equalizer; PA1 B. adding to the equalization error estimation value already obtained the newly obtained estimation value; and PA1 C. comparing the absolute value of a result of the addition of the equalization error estimation values with a predetermined convergent coefficient, if the absolute value of the result of the addition is smaller than the convergent coefficient, returning to step B, and if the absolute value of the result of the addition is larger than the convergent coefficient, varying a parameter of the automatic equalizer in accordance with the polarity of the estimation values.
For automatic equalization, an equalization error and its estimation value are obtained. A signal E(k) corresponding to the equalization error is given by: EQU E(k)=(X(k)-T)-B(k)
where
The equalization error estimation value (estimation value of equalization error) H(j) is given by: EQU H(j)+.SIGMA.A(k-i).times.sgn(E(k))
where
An appropriate number of occurrences of addition N for obtaining the estimation value H(j) is related to the S/N of the reproduced signal.
In the three-tap transversal filter, the coefficient of the filter is increased or decreased by an infinitesimal value .DELTA. in a direction for reducing intersymbol interference in accordance with signs of three estimation values. That is, filter coefficients as a result of automatic equalization are: EQU C(-1)=C(-1)-.DELTA..times.sgn(H(-1)) EQU C(0)=C(0)-.DELTA..times.sgn(H(0)) EQU C(1)=C(1)-.DELTA..times.sgn(H(1))
This is the procedure of automatic equalization by a three-tap transversal filter using the zero forcing.
In regard to convergence of parameters (filter coefficients), it is desired that convergence occurs in a short time immediately after the start of convergence or upon changes in status in which equalization errors may often occur that variation is small after sufficient convergence, which would result in a decrease in code errors. In order to meet these requirements, a method of changing the width of an increase or decrease .DELTA. in a nonlinear fashion in accordance with the magnitude of the estimation value of the equalization error H(j) is often used. For example, this is done as follows in accordance with, for example, the ratio between the estimation value H(j) and a convergence coefficient M (absolute value): EQU 0&lt;[H(j)/M]&lt;1:.DELTA.=1 EQU 1&lt;[H(j)/M]&lt;2:.DELTA.=2 EQU 2&lt;[H(j)/M]&lt;4:.DELTA.=3 EQU 4&lt;[H(j)/M]&lt;8:.DELTA.=4 EQU 8&lt;[H(j)/M]:.DELTA.=5
Hereinbelow, this method is called the variable step method. FIG. 4 shows this method in the form of a flow chart. In FIG. 4, reference numeral 31 denotes a clearing step for clearing k and H(j) to zero. Next, step 32 calculates estimation value H(j) of an error. Then the value of k is incremented by +1. Step 33 decides whether (k=N) or not, and calculates estimation values N times. After addition is done N times, steps of decision 34, 35, 36 and 37 are executed successively. These steps of decision 34 to 37 are those referred to above. When results of such decision are affirmative, the value .DELTA. is set to 1, 2, 3 and 4, respectively (steps 38, 39, 40 and 41). When all of the results of the decision are negative, control moves to step 42, and the value .DELTA. is set to 5. Then in step 43, the coefficient C(j) is increased or decreased by the width of .DELTA. determined in accordance with the polarity of the estimation value H(j).
This method certainly realizes fast convergence in the presence of numerous equalization errors, and decreases variation after sufficient convergence. However, it requires circuits (in case of hardware) or steps (in case of software) for varying .DELTA. in response to the value of [H(j)/M] as many as the number of parameters, and therefore increases the scale of the entire circuit or the processing time by software.
Previously, the present Applicant remarked the fact that it is possible to presume where errors may occur because of random noise, low range shut-off, high range shortage and so forth, and proposed a system for adaptively controlling a threshold value of an integral detecting scheme (U.S. Pat. No. 5,313,472 filed Jun. 4, 1992). The application shows that each parameter S and D of the system is equivalent to the filter coefficient of an automatic equalizer. Therefore, the following simulations are executed with the system instead of the automatic equalizer.
In regard to the automatic equalization employed in the adaptive threshold value detecting scheme referred to above, converging characteristics of parameters with varieties of the number of occurrences of addition N are illustrated. These are results of simulation by a computer by introducing a digital VTR reproduced signal A/D-converted in eight bits. The simulation system employs the adaptive threshold value detecting scheme after a linear equalizer, and automatically sets two parameters S and D, required for the detection, from the initial value 0 by using the zero forcing algorithm.
FIG. 1 shows a case where the number of occurrences of addition N is 100, while FIG. 2 shows a case where N=6400. Further, results of cases where N is 100 to 12800 are shown in a table of FIG. 3. In FIG. 3, each numeral in the column "occurrences of convergence" is the number of occurrences of repetition at the time when results become constant or periodic after simulating the same data repeatedly by using a parameter resulting from preceding convergence as the initial value. FIGS. 1 and 2 show values after reaching the condition as converged values.
According to the results of the simulation, although parameters converge early under N=100, variation after convergence is large, and code errors are numerous. Under N=6400, although variation after convergence is small and code errors are less, a relatively long time is required for convergence.
FIG. 5 shows an aspect of convergence of parameters S and D by the variable step method. FIG. 5 shows variation immediately before convergence and variation after convergence under (N=400 and M=32). FIG. 6 shows results of variation by changing N and fixing M to 32.